Update README.md

README.md

@@ -10,5 +10,52 @@ pinned: false
 license: apache-2.0
 short_description: ' CPU-only Zero-Shot Text Classification'
 ---
+# 🏷️ Zero-Shot Text Classification  
+[![Hugging Face Space](https://img.shields.io/badge/HuggingFace-Spaces-blue?logo=huggingface)](https://huggingface.co/spaces/your-username/zero-shot-classifier)  
+[![Gradio UI](https://img.shields.io/badge/Gradio-5.31.0-brightgreen?logo=gradio)]  
+[![Model](https://img.shields.io/badge/Model-BART--MNLI-orange)](https://huggingface.co/facebook/bart-large-mnli)  
+[![License](https://img.shields.io/badge/License-MIT-lightgrey)](LICENSE)
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+
+## 🚀 Overview  
+Unlock **zero-shot classification** for any text—no fine-tuning required.  
+Define your own label set on the fly and see how well each label matches your input, powered by **BART-MNLI** on CPU.
+
+> **AI buzzwords:**  
+> Natural Language Inference • Zero‐shot Learning • Transformer-based NLP • Real-time Inference • Edge Deployment • Cloud-native Demo
+
+---
+
+## ✨ Features
+
+| 🔑 Feature                     | 🔍 Description                                                  |
+|--------------------------------|----------------------------------------------------------------|
+| **🚀 Zero-Shot**                | Classify into arbitrary categories without task-specific data  |
+| **⚡ CPU-Only Inference**        | Runs on free Hugging Face Spaces (2 vCPU / 16 GB RAM)         |
+| **🎛️ Single vs. Multi-Label**   | Toggle between exclusive or overlapping labels                |
+| **🎨 Interactive UI**           | Gradio Blocks with text input, label list, mode toggle, table |
+| **🔧 No Training Needed**       | Leverages pre-trained BART-MNLI via HF Transformers           |
+| **☁️ Instant Deploy**           | Commit three files—Spaces auto-builds & hosts your demo       |
+
+---
+
+## 🏗️ How It Works
+
+1. **User Input** – Paste any sentence or paragraph.  
+2. **Label Definition** – Enter comma-separated candidate labels (e.g. “Positive, Negative, Question”).  
+3. **Model Inference** – Pipeline computes entailment scores for each label.  
+4. **Result Table** – Display each label with its confidence score.
+
+All done **locally on the Space**, ensuring **privacy**, **zero API cost**, and **lightning speed**.
+
+---
+
+## 🛠️ Local Development
+
+```bash
+git clone https://github.com/your-username/zero-shot-classifier.git
+cd zero-shot-classifier
+python3 -m venv venv && source venv/bin/activate
+pip install -r requirements.txt
+python app.py